Long-range-transported Canadian smoke layers in the stratosphere over
northern France were detected by three lidar systems in August 2017. The
peaked optical depth of the stratospheric smoke layer
exceeds 0.20 at 532 nm, which is
comparable with the simultaneous tropospheric aerosol optical depth. The
measurements of satellite sensors revealed that the observed stratospheric
smoke plumes were transported from Canadian wildfires after being lofted by
strong pyro-cumulonimbus. Case studies at two observation sites, Lille (lat
50.612, long 3.142, 60 m a.s.l.) and Palaiseau (lat 48.712, long 2.215,
156 m a.s.l.), are presented in detail. Smoke particle depolarization
ratios are measured at three wavelengths: over 0.20 at 355 nm, 0.18–0.19 at
532 nm, and 0.04–0.05 at 1064 nm. The high depolarization ratios and their
spectral dependence are possibly caused by the irregular-shaped aged smoke
particles and/or the mixing with dust particles. Similar results are found by
several European lidar stations and an explanation that can fully resolve
this question has not yet been found. Aerosol inversion based on lidar
2α+3β data derived a smoke effective radius of about
0.33 µm for both cases. The retrieved single-scattering albedo is
in the range of 0.8 to 0.9, indicating that the smoke plumes are absorbing.
The absorption can cause perturbations to the temperature vertical profile,
as observed by ground-based radiosonde, and it is also related to the ascent
of the smoke plumes when exposed in sunlight. A direct radiative
forcing (DRF) calculation is performed using the obtained optical and
microphysical properties. The calculation revealed that the smoke plumes in
the stratosphere can significantly reduce the radiation arriving at the
surface, and the heating rate of the plumes is about 3.5 K day−1. The
study provides a valuable characterization for aged smoke in the
stratosphere, but efforts are still needed in reducing and quantifying the
errors in the retrieved microphysical properties as well as radiative forcing
estimates.

Stratospheric aerosols play an important role in the global radiative budget
and chemistry–climate coupling
(Deshler, 2008; Kremser et al., 2016; Shepherd, 2007).
Volcanic eruption is a significant contributor of stratospheric aerosols
because the explosive force could be sufficient enough to penetrate the
tropopause, which is regarded as a barrier to the convection between the
troposphere and stratosphere. In addition to volcanic eruption, biomass burning has
been reported to be one important constituent of the increasing stratospheric
aerosols (Hofmann et al., 2009; Khaykin et al., 2017; Zuev et al., 2017). The
pyro-cumulonimbus clouds generated in intense fire activities have the
potential to elevate fire emissions from the planetary boundary layer to the
stratosphere (Luderer et al., 2006; Trentmann et al., 2006). Stratospheric
smoke plumes have been reported in many previous studies
(Fromm et al., 2000, 2005; Fromm and Servranckx, 2003; Sugimoto et al., 2010).

In the summer of 2017, intense wildfires spread in the west and north of
Canada. By mid-August, the burnt area had grown to almost 9000 km2 in
British Columbia, which broke the record set in 1958 (see
https://www.nceo.ac.uk/article/ the-2017-canadian-wildfires-a-satellite-perspective/, last access:
15 January 2019). The severe wildfires generated strong pyro-cumulonimbus
clouds, which were recorded by the satellite imagery MODIS (Moderate Resolution
Imaging Spectrometer). The GOES-15 (Geostationary Operational Environmental
Satellite) detected five pyro-cumulonimbus clouds in British Columbia on
12 August 2017 (see https://pyrocb.ssec.wisc.edu, last access:
15 January 2019). Smoke plumes in the troposphere and lower stratosphere were
observed by several European lidar stations in August and September 2017.
Ansmann et al. (2018) and Haarig et al. (2018) observed
stratospheric and tropospheric smoke layers originating from Canadian
wildfires on 21–23 August 2017 in Leipzig, Germany. The maximum extinction
coefficient of the smoke layers reached 0.5 km−1, about 20 times higher
than the observation 10 months after the eruption of the Pinatubo volcano in
1991 (Ansmann et al., 1997). Khaykin et al. (2018) reported
Canadian smoke layers in the stratosphere over southern France in August 2017
and they found that the smoke plumes can travel the whole globe (at middle
latitudes) in about 3 weeks.

Reoccurring aerosol layers in the troposphere and lower stratosphere were
detected by the lidar systems in northern France during 19 August and 12
September 2017. In this study, we present the stratospheric smoke
observations from two French lidar stations: Lille (lat 50.612, long 3.142,
60 m a.s.l.) and Palaiseau (lat 48.712, long 2.215,
156 m a.s.l.), and a mobile lidar system.
Satellite measurements from multiple sensors, including UVAI (ultraviolet
aerosol index) from the OMPS NM (Ozone Mapping and Profiler Suite, Nadir
Mapper), CO (carbon monoxide) concentration from AIRS (Atmospheric Infrared
Sounder),
and backscatter coefficient and depolarization ratio profiles from CALIPSO
(Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) help
identify the source and the transport pathway of the smoke layers. This study
is focused on the retrieval of the aerosol optical and microphysical
properties using lidar measurements. Further, the radiative effect of the
smoke layer is presented.

2.1 Lidar data processing

In this subsection, we present the method for processing lidar measurements
and the error estimation is presented in the Appendix. The Raman lidar technique
(Ansmann et al., 1992) allows an independent calculation of extinction
and backscatter coefficients. When the nitrogen Raman signal is not
available, the Klett method (Klett, 1985) is used to calculate the
extinction and backscatter coefficients, based on an assumption of the aerosol
lidar ratio.

In this study, the stratospheric aerosol layers are at high altitudes at
which
the signal-to-noise ratio of Raman channels is not sufficient to obtain a high-quality extinction profile; therefore, we choose the Klett method.

To reduce the dependence of Klett inversion on the assumption of lidar ratio,
we use a pre-calculated optical depth of the stratospheric aerosol layer as
an additional constraint. We test a series of lidar ratios in the range of
10–120 sr and apply independent Klett inversion with each lidar ratio at a
step of 0.5 sr. The integral of the extinction coefficient over the
stratospheric layer, expressed below, is compared with the pre-calculated
optical depth.

(1)τi(λ)=∫rbasertopαa(λ,r)dr,

where τi is the integral of extinction coefficient αa,
derived from Klett inversion. r is the distance, the subscripts “top” and
“base” represent the top and base of the stratospheric aerosol layer, and
λ is the lidar wavelength.

The pre-calculated optical depth is derived from the elastic channel at 355
and 532 nm. The method is widely used in cirrus cloud studies
(Platt, 1973; Young, 1995). By comparing the lidar signal with
the molecular backscattered lidar signal, we found there is only molecular
scattering below and above the smoke plumes. So we can calculate the optical
depth of the smoke plumes as below:

where τu is the optical depth of the stratospheric smoke layers.
P‾top and P‾base represent the
mean lidar signal at the top and the base of the stratospheric layer.
αm and βm are the molecular extinction and
backscatter coefficients. We calculate the lidar signal mean within a window
of 0.5 km at the top and the base of the aerosol layer to get
P‾(rtop,λ) and
P‾(rbase,λ). We use this method to estimate the
optical depth of the stratospheric layer for LILAS and IPRAL measurements.
The lidar ratio leading to the best agreement of τi and τu is
accepted as the retrieved lidar ratio of the stratospheric aerosol layer. We
apply Klett inversion only to the stratospheric aerosol layer, from 1 km
below the layer base to 1 km above the layer top. Therefore, the impact of
tropospheric aerosols is excluded. Compared to the Raman method, the extinction
and backscatter coefficients calculated from the Klett method are not independent
because of the assumed vertically constant aerosol lidar ratio. But in this
study, the smoke particles are well mixed, so the vertical variation in lidar
ratio is expected to be not significant. Additionally, using the Klett method
avoids the effects of vertical smoothing that occur to the Raman derived
extinction profile.

The particle linear depolarization ratio, δp, is written as

(3)δp=Rδv(δm+1)-δm(δv+1)R(δm+1)-(δv+1),

where R is the backscatter ratio, δv is the volume linear
depolarization ratio, and δm is the molecular depolarization
ratio. R is defined as the ratio of the total backscatter coefficient to
the molecular backscatter coefficient. δm=0.004 is used in
the calculation of particle linear depolarization ratio. δv
is the ratio of the perpendicularly backscattered signal to the parallel
backscattered signal, multiplied by a calibration coefficient. The
depolarization calibration is designed to calibrate the electro-optical ratio
between the perpendicular and parallel channels and is performed following the
procedure proposed by Freudenthaler et al. (2009). The particle
linear depolarization ratio is a parameter related to the shape of aerosol
particles, and it is usually used in the lidar community for aerosol typing.
The particle linear depolarization ratio of spherical particles is zero. For
irregular-shaped particles, for example ice particles in cirrus clouds, the
measured particle linear depolarization is about 0.40
(Sassen et al., 1985; Veselovskii et al., 2017).

2.2 Aerosol inversion and radiative forcing estimation

The 3β+2α from lidar observations can be inverted to obtain
particle microphysical parameters. The regularization algorithm is used to
retrieve size distribution, wavelength-independent complex refractive
indices, particle number, and surface and volume concentrations
(Müller et al., 1999; Veselovskii et al., 2002). We apply GRASP
(Generalized Retrieval of Aerosol and Surface Properties) to calculate the
DRF (direct radiative forcing) effect of the stratospheric aerosol layer.
GRASP is the first unified algorithm developed for characterizing atmospheric
properties gathered from a variety of remote-sensing observations. Depending
on the input data, GRASP can retrieve columnar and vertically resolved aerosol properties and surface
reflectance (Dubovik et al., 2014). As a branch of the GRASP algorithm,
GARRLiC (Generalized Aerosol Retrieval from Radiometer and Lidar Combined
data, called GARRLiC/GRASP hereafter) algorithm was developed for the
inversion of coincident single- or multi-wavelength lidar and sun photometer
measurements (Lopatin et al., 2013; Bovchaliuk et al., 2016). The two
main modules of GARRLiC/GRASP are the forward model and numerical inversion
module. The forward module simulates the atmospheric radiation by using
radiative transfer and by accounting for the interaction between light and
trace gases, aerosols, and underlying surfaces. The aerosol scattering
properties in the atmosphere are represented by one or two aerosol
components, whose optical properties can be described using a mixture of
spheres and spheroids and are vertically independent. The vertically resolved
optical properties, such as the extinction and backscatter coefficients etc.,
measured by lidar, are described by varying the aerosol vertical
concentration. The forward model includes a radiative transfer model in order
to simulate multiple types of observations. The radiative transfer equation
in GARRLiC/GRASP is solved using this parallel plane approximation. The
atmosphere is divided into a series of parallel planes and the optical
properties of each parallel plane can be represented by the input parameters.
The radiative transfer model is based on the study of
Lenoble et al. (2007). The numerical inversion module follows the
multi-term least-squares method strategy and derives a group of unknown parameters that
fits the observations.

In this study, we apply the forward model of GARRLiC/GRASP to estimate the
forcing effect of the observed stratospheric plume in contrast to a standard
Rayleigh atmosphere. The input parameters for DRF are the retrieved aerosol
microphysical properties from the regularization algorithm, including the size
distribution, the complex refractive indices, and the assumed sphere
fraction; the aerosol vertical distribution of the stratospheric plume; and
surface BRDF (bidirectional reflectance distribution function) parameters.
The forward model of GARRLiC/GRASP can produce downward and upward broadband
flux, covering the 0.2–4.0 µm spectrum, at vertical levels
specified by the users. Hence, we can calculate the DRF and the heating rate
specific to smoke plume.

3.1 Simultaneous lidar and sun photometer observations

LILAS (Lille Lidar Atmospheric Study) is a multi-wavelength Raman lidar
(Bovchaliuk et al., 2016; Veselovskii et al., 2016) operated at LOA
(Laboratoire d'Optique Atmosphérique, Lille, France). The LILAS system is
transportable and has three elastic channels (355, 532, and 1064 nm), with
the capability of measuring the depolarization ratios at these wavelengths.
Further, it has three Raman channels at 387, 408, and 530 nm. The IPRAL system
(IPSL Hi-Performance multi-wavelength Raman Lidar;
Bravo-Aranda et al., 2016; Haeffelin et al., 2005) is a multi-wavelength Raman
lidar operated at SIRTA (Site Instrumental de Recherche par
Télédétection Atmosphérique, Palaiseau, France). The distance between
the two systems is around 300 km. Lidar IPRAL has the same elastic channels
as LILAS, but the three Raman channels are 387, 408, and 607 nm. In the IPRAL
system, the depolarization ratio is only measured at 355 nm. The two lidar
systems were operated independently and both observed reoccurring smoke
layers in the lower stratosphere during the period from 19 August to
12 September 2017. In addition, sun photometer measurements are available at
Lille and Palaiseau, which are both affiliated stations of AERONET (AEROsol
RObotic NETwork). The LILAS and IPRAL lidar systems are affiliated with EARLINET
(European Aerosol Research Lidar Network)
(Bösenberg et al., 2003; Böckmann et al., 2004; Matthais et al., 2004; Papayannis et al., 2008; Pappalardo et al., 2014).
Both systems perform regular measurements and follow the standard EARLINET
data quality check and calibration procedures (Freudenthaler et al., 2018).

On 29 August, three lidar systems in northern France simultaneously observed
a stratospheric aerosol layer. The three lidar systems are LILAS, IPRAL, and a
single wavelength (532 nm) CIMEL micro-pulse lidar, which is set up in a
light mobile system, MAMS (Mobile Aerosol Monitoring System;
Popovici et al., 2018), to explore aerosol spatial variability. MAMS was
traveling between Palaiseau and Lille on 28 and 29 August. MAMS is equipped
with a mobile sun photometer, PLASMA (Photomètre Léger Aéroporté pour
la Surveillance des Masses d'Air, Karol et al., 2013), capable of
measuring columnar aerosol optical depth (AOD) along the route. The
configuration of the three lidar systems is summarized in
Table 1.

Table 1Three involved lidar systems and their
configuration and locations.

Figure 1 shows the normalized lidar range-corrected
signals and columnar AOD at 532 nm derived from sun photometer measurements
on 29 August 2017. The aerosol layers in the lower stratosphere, stretching
from 16 to 20 km, were detected by the three lidars. The IPRAL lidar system
in Palaiseau detected the aerosol layer in the range of 16–20 km on
29 August. The columnar AOD showed no significant variations, staying between
0.30 and 0.40, from 10:00 to 16:00 UTC and started decreasing from
17:00 UTC. Along the route from Palaiseau to Lille, MAMS lidar observed a layer
between 16 and 20 km consisting of two well-separated layers. The columnar
AOD was very stable, around 0.40, all along the route from Palaiseau to
Lille. Lidar LILAS in Lille observed a shallow layer between 18 and 20 km at
about 08:00 UTC on 29 August. The thickness of the layer increased to 4 km
until 16:00 UTC. The columnar AOD increased from 0.20 to 0.40 from 08:00 to
14:00 UTC. The lidar quick look indicated that the aerosol content in the
lower troposphere did not show significant variations during 08:00 and
12:00 UTC, so the increased optical depth, 0.2, came mainly from the
contribution of the stratospheric aerosol layer.

Figure 1Lidar range-corrected signal and columnar AOD from the sun photometer at
532 nm on 29 August 2017. (a) IPRAL system in Palaiseau.
(b) MAMS lidar en route from Palaiseau to Lille. (c) LILAS
in Lille. Columnar AOD measurements are interpolated from AERONET (Lille and
Palaiseau) and PLASMA (mobile system) measurements. MAMS started from
Palaiseau at 13:53 UTC and arrived in Lille at 16:23 UTC. The departure and
arriving times are indicated in (a) and (c) with the
dashed white lines.

Figure 2 shows the lidar range-corrected signal at
1064 nm on 24–25 August 2017. The plume between 17 and 18.5 km is the
smoke layer. Due to cirrus clouds and low clouds in the troposphere, the
lidar signals in the plume are interrupted. In the nighttime, the plume base
is stable at about 17 km. Just starting from the sunrise time at 04:51 UTC,
a gradual and obvious ascent is observed. In 3–4 h, the plume base ascended
by about 0.6 km. Between 10:00 and 16:00 UTC, the plume base stayed stable.
The ascent of smoke plume was also presented in Ansmann et al. (2018) and
Khaykin et al. (2018). Khaykin et al. (2018) mentioned
that the plume ascended very fast during the first few days after being
injected into the troposphere. Based on the observation in
Fig. 2, we derived the ascent rate of approximately
2.1–2.8 km per day, considering that the sunshine duration is 14 h
(according to the latitude of Lille site) and that the vertical speed of the
plume is constant. Ansmann et al. (2018) explained that the ascent of the
plume may be related to the absorption of soot-containing aerosols and the
wind velocity in the stratosphere. Figure 2 shows
that the plume does not continuously ascend in the daytime. One possible
explanation we infer is that the self-heating and the wind shear reached an
equilibrium point in the plume, so it moved neither upward nor downward.

Figure 2Lidar range-corrected signal at 1064 nm on
24–25 August 2017 measured by LILAS. The solid red line indicates the
sunrise time. The two dashed red lines point out the approximate layer base
before and after the sunrise. The sunrise and sunset times are 04:51 and
20:47 UTC, respectively. The corresponding daytime duration is about 14 h.

3.2 Radiosonde measurements

We take the radiosonde measurements from two stations closest to the lidar
sites: Trappes (48.77∘ N, 1.99∘ E, France) and Beauvechain
(50.78∘ N, 4.76∘ E, Belgium). Trappes is about 20 km from
Palaiseau and Beauvechain is 120 km from Lille. Considering the large
spatial distribution of the stratospheric aerosols, it is obvious that the
radiosonde passed through this stratospheric smoke layer.
Figure 3 shows the temperature at 00:00 and
12:00 UTC on 29 August for Trappes and 21:00 UTC on
29 August for Beauvechain. To compare, we plot the temperature profile of
Trappes at 12:00 UTC on 21 August, when no stratospheric aerosol layers
presented. The temperature profiles clearly show an enhancement between 16
and 20 km, which coincides with the altitude at which the stratospheric
plumes appear. The spatial–temporal occurrence of this temperature
enhancement and the stratosphere plume at two independent stations indicate
that they are directly correlated.
Fromm et al. (2005, 2008) also presented temperature
increase in the stratospheric smoke layers.

Figure 3Temperature profiles from the radiosonde
measurements. The green and cerulean lines are the temperature profiles of
Trappes at 00:00 and 12:00 UTC on 29 August 2017. The red line shows the
Beauvechain data at 21:00 UTC on 29 August 2017. The black line is for
12:00 UTC on 21 August in Trappes. The horizontal dashed black line at 13 km
represents the approximate position of the tropopause.

3.3 MODIS measurements

MODIS is a key instrument onboard the Terra and Aqua satellites. Terra MODIS
and Aqua MODIS view the entire Earth's surface every 1 to 2 days.
Several episodes of Canadian wildfires have been observed by MODIS since
early July 2017. On 12 August, MODIS observed a thick grey plume arising
from British Columbia in the west of Canada (not shown; please see the
web page of WorldView:
https://worldview.earthdata.nasa.gov, last access:
15 January 2019). Figure 4 shows the Earth's true color
image overlaid with the fires and thermal anomalies on 15 August 2017 when
the plumes had spread over a large area. The region marked with the green
dashed line is a huge visible smoke plume and in its southwest MODIS
detected a belt of fire spots. Additionally, during the week of
13–19 August, MODIS (see
https://worldview.earthdata.nasa.gov, last access:
15 January 2019)
observed a widespread cloud coverage over Canada and showed that cloud
layers were overshadowed by the smoke plumes, meaning that the plumes were
lofted above the cloud layers, as shown in Fig. 4.

3.4 OMPS NM UVAI maps

UVAI is a widely used parameter in characterizing UV-absorbing aerosols, such
as desert dust, carbonaceous aerosols coming from anthropogenic biomass
burning, wildfires, and volcanic ash. The UVAI is determined using the 340 and
380 nm wavelength channels and is defined as

(4)UVAI=-100×log10I340I380meas-log10I340I380calc,

where I340 and I380 are the backscattered radiance at 340 and
380 nm. The subscript “meas” represents the measurements and “calc”
represents the calculation using a radiative transfer model for pure Rayleigh
atmosphere. The UVAI is defined so that positive values correspond to
UV-absorbing aerosols and negative values correspond to non-absorbing
aerosols (Hsu et al., 1999). The OMPS NM on board the Suomi NPP
(National Polar-orbiting Partnership) is designed to measure the total column
ozone using backscattered UV radiation between 300 and 380 nm. A
110∘ FOV (field-of-view) telescope enables full daily global coverage
(McPeters et al., 2000; Seftor et al., 2014). Figure 5
shows the evolution of UVAI from OMPS NM (Jaross, 2017) every 2 days
from 11 to 29 August 2017. The evolution of the UVAI during this event has
also been shown in the study of Khaykin et al. (2018). A plume
with relatively high UVAI first occurred over British Columbia on 11 August,
and the intensity of the plume was moderate. An obvious increase in UVAI from
11 to 13 August was observed over the northwest of Canada. It is a clear
indication that the events on 12 August were responsible for the increase in
UVAI. From 13 to 17 August, the plume spread in the northwest–southeast
direction and the UVAI in the center of the plume reached 10. On 19 August,
the plume center reached the Labrador Sea and the forefront of the plume
reached Europe. From 21 to 29 August, the UVAI in the map was much lower than
the previous week. During this period, we can still distinguish a plume
propagating eastward from the Atlantic to Europe, with the UVAI damping
during the transport. Figure 5e–j show that Europe was
overshadowed by the high-UVAI plume during 19 and 29 August.

Figure 5OMPS NM daily UVAI products from 11 to
29 August 2017. The results are plotted every 2 days. Grey indicates
areas with no retrievals.

3.5 AIRS CO maps

AIRS is a continuously operating cross-track scanning sounder on board NASA's
Aqua satellite launched in May 2002. AIRS covers the 3.7 to 16 µm
spectral range with 2378 channels and a 13.5 km nadir FOV
(Susskind et al., 2014; Kahn et al., 2014). The daily coverage of AIRS
is about 70 % of the globe. AIRS is designed to measure the water
vapor and temperature profiles. It includes the spectral features of the key
carbon trace gases, CO2, CH4, and CO
(Haskins and Kaplan, 1992). The current CO product from AIRS is very mature
because the spectral signature is strong and the interference of water vapor
is relatively low (McMillan et al., 2005). CO, as a product of the burning
process, can be taken as a tracer of biomass burning aerosols
(Andreae et al., 1988) due to its relatively long lifetime of
0.5 to 3 months. CO can also
originate from anthropogenic sources, for example engines of vehicles
(Vallero, 2014). In August 2017, the wildfire activities were
so intense that the CO plumes rising from the fire region were much more
significant than the background. This strong contrast makes CO a good tracer
for the transport of the smoke plumes.

Figure 6Total CO concentration
(molecules cm−2) retrieved from AIRS. The maps are plotted every 2
days from 11 to 29 August 2017.

Figure 6 shows the evolution of the total column CO
concentration (Texeira, 2013) every 2 days during the period of 11 to
29 August 2017. CO concentration strongly increased in the west and north of
Canada from 11 to 13 August, similar to the UVAI shown in Fig. 5. The forefront of the CO plume
reached the west and north of Europe since 19 August. We find that the
spatial distribution and temporal evolution of CO are strongly co-related
with the UVAI. This correlation is very evident before 21 August. After
21 August, the correlation became weaker, for the UVAI in North America was
decreasing fast while the CO concentration remained almost unchanged or
decreased much more slowly. This is possibly due to the longer lifetime of CO
compared to UVAI. Combing the MODIS image and the UVAI and CO
spatial–temporal evolution, we conclude that the aerosol plumes observed in
Europe were smoke transported from Canada.

3.6 CALIPSO measurements

CALIPSO measurements provide a good opportunity to investigate the vertical
structure of the plumes and trace back the transport of the plumes. CALIPSO
measures the backscattered signal at 532 and 1064 nm. One parallel channel
and one perpendicular channel are coupled to derive the particle linear
depolarization ratio at 532 nm. Figure 7a–f
present the profiles of the backscatter coefficient and particle linear
depolarization ratio at 532 nm, corresponding to the six locations a–f
in Fig. 4. These data were obtained from the NASA
Langley Research Center Atmospheric Science Data Center. The six locations
are intentionally selected, falling in the region with elevated UVAI and CO
concentration and following the transport pathway of the plume (in
Figs. 5 and 6) from Canada to Europe.
Figure 7 shows the enhancements of backscatter in
the upper troposphere and lower stratosphere. Aerosol and cloud are both
possible causes of the backscatter enhancements and can be distinguished by
using the particle depolarization ratio. We have examined the temperature
profiles over several sites in North America in August 2017 and found that,
above 10 km, the temperature drops below −38 ∘C; at this
temperature clouds consist mainly of ice crystals. The particle
depolarization ratio is usually no less than 0.40 for ice cloud and from a
few percent to about 0.40 for mixed-phase cloud.

Figure 7The profiles of backscatter coefficient
and particle linear depolarization ratio (PLDR) at 532 nm from CALIPSO.
Panels (a)–(f) correspond to the six locations
(a)–(f) in Fig. 4. The
corresponding CALIPSO tracks are (a) 09:50:19, 14 August 2017;
(b) 08:54:37, 15 August 2017; (c) 07:03:13, 17 August 2017;
(d) 06:50:44, 19 August 2017; (e) 03:20:25, 21 August 2017;
and (f) 01:29:01, 23 August 2017. A total of 20 profiles are averaged over
these six locations. The solid green and pink lines represent backscatter
coefficient and particle linear depolarization ratio, respectively. The red
squares with error bars represent the mean particle linear depolarization
ratio and the standard deviation within each layer.

Figure 7a and b show the aerosol layers observed on
14 and 15 August over the north of Canada; both locations lay in the area
where MODIS observed a smoke plume on
15 August (Fig. 4) and the area with high UVAI and
CO concentration. The particle linear depolarization ratio is about 0.05 in
Fig. 7a and 0.10 in
Fig. 7b, meaning that it is an aerosol layer instead
of ice or mixed-phase cloud. Figure 7c and f show
stratospheric layers detected at 10–20 km in height, with the depolarization
varying from 0.10 to 0.18. The lower layer at about 9 km in
Fig. 7d has a depolarization ratio between 0.20 and
0.45 (median 0.32), which falls into the category of ice or mixed-phase
clouds. Profiles in Fig. 7f were captured over
Berlin at 01:29 UTC on 23 August. About 150 km to the southwest, a lidar
in Leipzig measured stratospheric smoke layers
(Haarig et al., 2018). The particle depolarization ratio of
CALIPSO at 532 nm on 23 August is consistent with ground-based lidar
measurements in Lille and Leipzig, which will be presented in Sect. 4. It
should be noted that aerosol types of the plumes in
Fig. 7 are quite uncertain in the CALIPSO product. These
layers are classified into scattered aerosol types, such as polluted dust,
elevated smoke, and volcanic ash. This misclassification could introduce some
extent of errors to the backscatter profile and particle depolarization
profiles.

4.1 Overview of retrieved optical parameters

We selected and averaged the lidar measurements in 10 time intervals, among which five periods are from the LILAS
system in Lille: 22:00 (24 August)–00:30 UTC (25 August);
13:00–16:00 UTC, 16:00–18:00 UTC (29 August); 20:00–23:00 UTC
(31 August); and 23:00 (31 August)–02:00 UTC (1 September); two intervals
are from the IPRAL system in Palaiseau: 16:00–18:00 and 19:20–21:20 UTC
(28 August). Three intervals are from the mobile lidar in the MAMS system
(29 August): 14:00–15:00 UTC (corresponding spatially to a 100 km distance
from Palaiseau to Compiègne), 15:00–15:45 UTC (100 km on the route from
Compiègne to Arras), and 16:15–16:30 UTC at Lille.

Figure 8 shows the optical depth of the stratospheric layer
varying from 0.05 to 0.23 (at 532 nm). The spectral dependence of the
optical depth of 355 and 532 nm is very weak. The maximal optical depth of
the stratospheric layer was observed in the afternoon of 29 August, between
16:00 and 18:00 UTC. The LILAS system observed AOD of
0.20±0.04 at 355 nm and 0.21±0.04 at 532 nm. As discussed
in Sect. 3.1, the columnar AOD at 532 nm from AERONET
increased by about 0.20 after the presence of the stratospheric layer, which
agrees well with the derived optical depth of the stratospheric layer. The
minimum of the optical depth appeared in the night of 31 August 2017, giving
0.04±0.02 at 355 nm and 0.05±0.02 at 532 nm. The optical depth
of the stratospheric layer along the route, observed by MAMS, is as follows:
0.19 over a distance of 100 km north from Palaiseau, 0.23 along 100 km of
the middle of the transect from Compiègne to Arras, and 0.22 when arriving
at Lille.

Due to the insufficient signal-to-noise ratio above the stratospheric plume,
the MAMS lidar measurements are processed using the Klett method and constrained
by
the columnar AOD measured by the PLASMA sun photometer. Klett inversion is
performed on the lidar profile from the surface to the top of the
stratospheric layer, assuming a vertically independent lidar ratio. The
optical depth of the stratospheric smoke layer is then calculated from the
integral of the extinction profile. As a result, the error of the estimated
smoke optical depth from MAMS measurements is difficult to quantify. Here we
present the optical depth from MAMS lidar for a comparison.

Table 2Retrieved lidar ratios (LRs), particle linear depolarization
ratios (PLDRs), layer thickness, and mean extinction coefficients from multi-wavelength lidar systems LILAS in Lille and IPRAL
in Palaiseau. α¯ is the mean extinction coefficient in the
stratospheric smoke layer. ΔL is the thickness of the stratospheric
smoke layer. The values after “±” represent the errors. Error
estimation is presented in the Appendix.

Table 2 summarizes the lidar ratio and particle
depolarization ratio in the stratospheric aerosol layer. Lidar ratios vary
between 54±9 and 58±23 sr at 532 nm and between 31±15 and
45±9 sr at 355 nm. The results from two different lidar systems and
with different observation times agree well, indicating that the properties
of the stratospheric layer are spatially and temporally stable. We derived a
higher lidar ratio at 532 than at 355 nm, which is a characteristic feature
of aged smoke and has been observed in previous studies
(Wandinger et al., 2002; Murayama et al., 2004; Müller et al., 2005; Sugimoto et al., 2010).
In the night of 31 August, the error of lidar ratio is about
30 %–35 %, relatively higher than the other days because of the low
optical depth. Although the error varies, the mean values of derived lidar
ratios are relatively stable. The
particle depolarization ratio decreases as wavelength increases. At 1064 nm,
the particle linear depolarization
ratio is very stable, varying from 0.04±0.01 to 0.05±0.01. At
532 nm, the particle linear depolarization ratio is also stable, varying from 0.18±0.03 to
0.20±0.03. The particle linear depolarization ratio at 355 nm increased
from 0.23±0.03 on 24 August to 0.28±0.08 on 31 August. However, the
increase is within the range of the uncertainties. The particle
depolarization ratio at 532 nm is in good agreement with CALIPSO
observations shown in Fig. 7c–f. The particle
depolarization ratio at 355 nm measured by LILAS is consistent with the
IPRAL system. Haarig et al. (2018) measured 0.23 at 355 nm, 0.18
at 532 nm, and 0.04 at 1064 nm in the stratospheric smoke layers on
22 August 2017, showing excellent agreements with our study.

The errors of particle depolarization ratio are calculated with the method in
the Appendix. The estimated errors of the particle depolarization ratio are
generally below 15 %, except the 355 nm channel in the night of
31 August when the optical depth was the lowest in all the investigated
observations in this study. On 31 August, the backscatter ratio, volume
depolarization ratio, and molecular depolarization ratio at 355 nm are
approximately: 3.5 (50 %), 0.15 (10 %), and 0.004 (200 %). The
values in the parentheses are the relative errors of the quantity on their
left. The resulting error of particle depolarization is about 28 %. At
532 nm, we derive 12 % of error for the particle depolarization
ratio when the backscatter ratio, volume depolarization ratio, and molecular
depolarization ratio are 10 (50 %), 0.15 (10 %), and 0.004
(200 %). In the same way, we derive less than 11 % of error for the
particle depolarization ratio at 1064 nm. The error at 355 nm is estimated
to be higher than 532 and 1064 nm as the interferences of molecular
scattering are stronger at this channel. When the layer is optically thicker,
for example, on 24 August, the error of 355 nm is estimated to be less than
13 %. Conservatively, we use 30 % for the error of the particle linear
depolarization ratio at 355 nm on 31 August and 15 % for the error of
the rest.

4.2 Case study

4.2.1 Optical properties

We select the night measurements of 24 August in Lille and 28 August in
Palaiseau as two examples. The two systems were operating independently, so
that the results from two different systems that measured at different times
can be regarded as verifications for each other.

Figure 9(a) Extinction and backscatter
coefficients, (b) particle linear depolarization ratio (PLDR), and the
extinction-related Ångström exponent (EAE) and backscatter-related
Ångström exponent (BAE) retrieved from LILAS observations between
22:00 UTC on 24 August 2017 and 00:30 UTC on 25 August 2017 at Lille. The errors
of extinction, backscatter coefficient, and corresponding Ångström
exponent at 355 and 532 nm are attributed to the error of the optical
depth.

24 August 2017, Lille

Figure 9 shows the retrieved optical properties of the
stratospheric smoke layer observed by the LILAS system in the night of
24 August in Lille. The stratospheric aerosol layer is between 17 and 18 km,
and we retrieved the extinction and backscatter profiles by assuming that the
lidar ratios are 36 sr at 355 nm and 54 sr at 532 nm. The lidar ratio at
1064 nm is assumed to be 60 sr. The extinction coefficient within the layer
is about 0.12–0.22 km−1 at 355 and 532 nm. It should be noted that
the profile of the extinction coefficient is similar to the backscatter
coefficient profile because we assume the aerosol lidar ratio is vertically
constant within the smoke layer. A comparison of backscatter coefficient
profile has been made (not shown) between Klett and Raman methods. We found
that the difference of the backscatter coefficient profiles from the two
methods is very minor,
indicating that our results are reliable. Assuming a vertically constant
aerosol lidar ratio in the smoke layer is not unrealistic, as one can see
that the particle linear depolarization ratios in the smoke layer have no
noticeable vertical variation, indicating that the smoke particles are well
mixed. The extinction-related Ångström exponent for 355 and 532 nm is
around 0.0±0.5; the backscatter-related Ångström exponent at
corresponding wavelengths is about 1.0±0.5. The particle depolarization
ratios decrease as wavelength increases: 0.23±0.03 at 355 nm,
0.20±0.03 at 532 nm, and 0.05±0.01 at 1064 nm. No parameters in
Fig. 9b exhibit noticeable vertical variations.

28 August 2017, Palaiseau

Figure 10 shows the retrieved optical parameters
from IPRAL observations at 19:20–21:20 UTC on 28 August 2017 in Palaiseau.
The thickness of the stratospheric layer is about 2.3 km, spreading from
17.2 to 19.5 km. Klett inversion was applied with an estimated lidar ratio of
36 sr at 355 nm and 58 sr at 532 nm. At 1064 nm the lidar ratio was
assumed to be 60 sr. The maximum extinction coefficient in the layer reached
0.12 km−1 at 532 nm. The extinction-related Ångström exponent
between 355 and 532 nm is about -0.06±0.5. The corresponding backscatter
Ångström exponent is about 1.2±0.5. The particle linear
depolarization ratio at 355 nm is about 0.27±0.05. The particle linear
depolarization ratio at 355 nm and extinction and backscatter-related
Ångström exponents between 355 and 532 nm do not show evident vertical
variations.

4.2.2 Microphysical properties

A regularization algorithm is applied to the vertically averaged extinction
coefficients (at 355 and 532 nm) and backscatter coefficients (at 355, 532,
and 1064 nm) in Figs. 9 and
10. Treating nonspherical particles is a challenging
task. Many studies have been performed to model the light scattering of
nonspherical particles. The spheroid model was used to retrieved dust
properties
(Dubovik et al., 2006; Mishchenko et al., 1997; Veselovskii et al., 2010).
Both sphere and spheroid models are used to retrieve particle microphysical
properties in our study. The retrievals using
sphere and spheroid models are rather consistent except the imaginary part of
the refractive index. The spheroid model tends to underestimate the imaginary
part of the complex refractive indices, if the measured particle
depolarization ratios are used. This demonstrates the deficiency of the
spheroid mode in retrieving highly-absorbent and irregular-shaped smoke
particles. The size of smoke particles is expected to be not very big so that
a sphere model should be able to provide reasonable results. The particle
linear depolarization ratio is not used in the retrieval, and the spectral
dependence of complex refractive indices is also ignored. The derived
effective radius (Reff), volume concentration (Vc),
and real (mR) and imaginary (mI) parts of the
refractive indices are summarized in Table 3.

Table 3Retrieved microphysical properties using
the lidar data in Lille and Palaiseau. Extinction and backscatter
coefficients shown in Figs. 9a and
10a are averaged in the range of 17–18.0 and
17.5–19.5 km, respectively. The averaged extinction and backscatter
coefficients are used as the input of the regularization algorithm to retrieve
particle microphysical properties.

The retrieved particle size distributes in the range of 0.1 to
1.0 µm, with an effective radius (volume-weighted sphere radius) of
0.33±0.10 for both Palaiseau data and Lille data. The volume
concentration is 15±5µm3 cm−3 for Palaiseau data
and 22±8µm−3 cm3 for Lille data. The complex refractive
indices retrieved from Lille and Palaiseau data are also in good agreement, giving 1.55±0.05 and
1.52±0.05 for the real part and 0.028±0.014 and 0.021±0.010 for
the imaginary part. The single-scattering albedos are estimated to be
0.82–0.89 for Lille data and 0.86–0.90 for Palaiseau data. The derived
aerosol microphysical properties from Palaiseau and Lille data are
consistent.

The errors of the retrieved parameters have been discussed in the relevant
papers
(Müller et al., 1999; Veselovskii et al., 2002; Pérez-Ramírez et al., 2013).
About 30 % of relative error is derived for the effective radius and
volume concentration, ±0.05 (absolute value) is expected for the real
part of refractive indices, and 50 % is derived for the imaginary part of
refractive radius. In our case, one significant limitation is that using a
sphere model does not allow us to reproduce the particle depolarization
ratios. We input the retrieved size distribution (not shown) and complex
refractive indices in Table 3 into the spheroid
model, and we found that the spheroid model (85 % spheroid and 15 %
sphere) can reproduce the spectral depolarization ratios with satisfactory
accuracy: 0.21, 0.19, and 0.07 at 355, 532, and 1064 nm, respectively.
However, the argument is not enough to justify that the aforementioned
uncertainty estimation from previous researchers is also applicable to our
retrievals. We provide this estimate as a reference, but at the current stage, we
are not able to provide more quantitative and accurate error estimation for
the retrieved microphysical properties.

4.2.3 Direct radiative forcing effect

The stratospheric plumes observed on 24 and 28 August in Lille and Palaiseau
are optically thick, with an extinction coefficient about 10 times higher than
in the volcanic ash observed by Ansmann et al. (1997) in April 1992,
10 months after the eruption of Mount Pinatubo. The radiative forcing imposed
by the observed layers poses a curious question. We input the retrieved
microphysical properties into GARRLiC/GRASP to estimate the DRF effect of the
stratospheric plumes in Lille and Palaiseau. We assume the vertical volume
concentration of aerosols follows the extinction profile in
Figs. 9 and 10. The surface
BRDF parameters for Lille and Palaiseau are taken from AERONET. The upward
and downward flux and efficiencies as well as the net DRF (ΔF, with
respect to a pure Rayleigh atmosphere) of the stratospheric aerosol layers
are calculated and Table 4 shows the daily
averaged net DRF (W m−2) at four levels: at the bottom of the
atmosphere (BOA), below the stratospheric layer, above the stratospheric
layer, and at the top of the atmosphere (TOA). For the layer observed in Lille
on 24 August, the top and base of the stratosphere are selected as 18.4 and
16.7 km and for Palaiseau observations they are 20 and 17.0 km.

Table 4Daily averaged net DRF flux
calculated by GARRLiC/GRASP. Aerosol microphysical properties in
Table 3 and aerosol vertical distributions in
Figs. 9a and 10a are used
to calculate the DRF effect at the following four vertical levels.

At the TOA, the net DRF flux is estimated to be −1.2 and
−3.5 W m−2 for Lille and Palaiseau data,
respectively. The corresponding forcing efficiencies are −7.9 and
−21.5 W m−2τ−1. At the BOA, the net DRF flux is estimated to
be −12.3 W m−2 for Lille data and −14.5 W m−2 for Palaiseau
data. The corresponding forcing efficiencies are −79.6 and
−89.6 W m−2τ−1. We noticed that the difference in net DRF
flux between the layer top and layer base is significant. For Lille data, we
obtained 9.9 W m−2 of difference between the top and the base of the
stratospheric layer and for Palaiseau, we obtained 11.1 W m−2. Because
of the high imaginary part of the refractive indices, the stratospheric
aerosols have the capacity of absorbing the incoming radiation, thus reducing
the upward radiation at the top of the stratospheric layer and the downward
radiation at the base of the stratospheric aerosol layer. The heating rate of
the stratospheric layer is estimated to be 3.3 K day−1 for Palaiseau
data and 3.7 K day−1 for Lille data. This qualitatively explains the
increase in temperature within the stratospheric layer, as observed by the
radiosonde measurements shown in Fig. 3. Due to high
uncertainty in the retrieved particle microphysical properties, the
uncertainty of the calculated DRF could be large.

The measurements revealed high particle depolarization ratios in the
stratospheric smoke at 355 and 532 nm. In particular, the particle
depolarization ratio at 355 nm ranges from 0.23±0.03 to 0.28±0.08,
while at 532 nm it is about 0.19±0.03. The depolarization ratio at
1064 nm is significantly lower, about 0.05±0.01. Similar spectral
dependences of depolarization ratios, 0.20, 0.09, and 0.02 at 355, 532, and
1064 nm, respectively, were observed by Burton et al. (2015) in a
smoke plume at 7–8 km in altitude (on 17 July 2014) in North American
wildfires. Particle depolarization ratios of 0.07 and 0.02 at 532 and
1064 nm, respectively, were observed in a Canadian smoke plume at 6 km (on
2 August 2007) over the US (Burton et al., 2012). In
Burton et al. (2012) and Burton et al. (2015), the smoke
traveled approximately 3 days and 6 days, respectively. The travel times in
both cases are shorter than in our study. The light-scattering process
leading to high particle depolarization ratios of smoke particles
has not been revealed yet. In
previous studies, smoke mixed with soil particles was suggested to be the
explanation
(Fiebig et al., 2002; Murayama et al., 2004; Müller et al., 2007a; Sugimoto et al., 2010; Burton et al., 2012, 2015; Haarig et al., 2018).
Strong convections occurring in fire activities in principle are capable of
lifting soil particles into the smoke plume (Sugimoto et al., 2010).

A high depolarization ratio with similar spectral dependence has been
observed in fine dust particles. Miffre et al. (2016) measured the particle
depolarization ratio of two Arizona Test Dust samples at backscattering
angle. The radii of the dust samples are mainly below 1 µm. They
obtained a higher depolarization ratio at 355 nm than at 532 nm, and the
depolarization ratios at both wavelengths are over 0.30. The sharp edges and
corners in the artificial dust samples are a possible reason for the measured
high particle depolarization ratio. In the study of
Järvinen et al. (2016), over 200 dust samples were used to measure the
near-backscattering (178∘) properties and it is found that, for
fine-mode dust, the particle depolarization ratio has a strong size
dependence. Järvinen et al. (2016) obtained about 0.12–0.20 and
0.25–0.30 for the depolarization ratio for equivalent particle size
parameters at 355 and 532 nm. Sakai et al. (2010) measured the
depolarization of Asian and Saharan dust in the backscattering direction and
obtained 0.14–0.17 at 532 nm for the samples with only sub-micrometer
particles and 0.39 for the samples with high concentrations of
super-micrometer particles. Mamouri and Ansmann (2017) concluded that the
depolarization spectrum of fine dust is 0.21±0.02 at 355 nm,
0.16±0.02 at 532 nm, and 0.09±0.02 at 1064 nm. This spectrum is
very similar to the Canadian stratospheric smoke aerosol presented in this study and Haarig et al. (2018).

However, Murayama et al. (2004) suggested that the coagulation
of smoke particles to the clusters with complicated morphology is a more
reasonable explanation because they found no signature of mineral dust after
analyzing the chemical compositions of the smoke sample.
Mishchenko et al. (2016) modeled the spectral depolarization ratios
observed by Burton et al. (2015) and found that such behavior
results from complicated morphology of smoke particles.
Kahnert et al. (2012) modeled the optical properties of light-absorbing
carbon aggregates (LACs) embedded in a sulfate shell. It was found that the
particle depolarization ratio increases with the aggregate radius
(volume-equivalent sphere radius). For the case of 0.4 µm aggregate
radius and 20 % LAC volume fraction, the computed depolarization ratios
are 0.12–0.20 at 304.0 nm, 0.08–0.18 at 533.1 nm, and about 0.015 at
1010.1 nm, which are comparable with the results in this study and
Haarig et al. (2018). In this study, we are not able to assess
which is the dominant factor leading to the high depolarization ratios,
possibly both the soil particles and smoke aging process are partially
responsible.

The derived lidar ratios are from 31±15 to 45±9 sr for 355 nm and
from 54±12 to 58±23 sr for 532 nm. Considering the uncertainties of
the lidar ratio, the derived values and the spectral dependence agree well
with previous publications (Müller et al., 2005; Sugimoto et al., 2010; Haarig et al., 2018) about aged smoke observations.
Haarig et al. (2018) obtained about 40 sr at 355 nm and 66 sr
at 532 nm, using the Raman method. The retrieved effective radius is about
0.33±0.10µm, consistent with the particle size obtained by
Haarig et al. (2018). The particle size is larger than the values
of fresh smoke observed near the fire source
(O'Neill et al., 2002; Nicolae et al., 2013). In particular, the
retrieved particle size agrees well with the observed smoke transported from
Canada to Europe (Wandinger et al., 2002; Müller et al., 2005).
Müller et al. (2007b) found that the effective radius increased
from 0.15–0.25 µm (2–4 days after the emission) to
0.3–0.4 µm after 10–20 days of transport time, which is
consistent with our results. But it is worth noting that
Müller et al. (2007b) investigated only tropospheric smoke and it
is not clear if this effect of the aging process is applicable to
stratospheric smoke.

The real part of the refractive indices obtained in this study is
1.52±0.05 for Palaiseau data and 1.55±0.05 for Lille data, without
considering the spectral dependence. The values are consistent with the
results for tropospheric smoke
(Dubovik et al., 2002; Wandinger et al., 2002; Taubman et al., 2004; Müller et al., 2005).
As for the imaginary part, we derived 0.021±0.010 from Palaiseau data and
0.028±0.014 from Lille data. The imaginary part of refractive indices of
smoke in previous studies is diverse. Müller et al. (2005) reported the
imaginary part varying around 0.003 for non-absorbing tropospheric smoke
originating from aged Siberian and Canadian forest fires.
Wandinger et al. (2002) obtained 0.05–0.07 for the imaginary part of
Canadian smoke in the troposphere over Europe. Dubovik et al. (2002)
derived about 0.01 to 0.03 for the imaginary part of biomass burning using
photometer observations. The retrieved imaginary part in our study falls into
the range of previously reported values. Using a sphere model in the
inversion is potentially an important error source, as spheres cannot fully
represent the scattering of irregular aged smoke particles. The application
on dust particles (Veselovskii et al., 2010) demonstrated that
retrieved volume concentration and effective radius are still reliable and
the main error is attributed to the imaginary part of the refractive index.
Errors in the optical data are also a potential error source of the retrieved microphysical parameters.

The relative humidity in the smoke layer is one factor that impacts the
refractive indices, the particle depolarization ratio, and lidar ratio of
smoke particles. However, in some studies, the relative humidity is not
mentioned, thus making the comparison difficult. Special attention should be
paid to the relative humidity when comparing the complex refractive indices.
Mixing with other aerosol types during transport is also a potential cause of
the modification of aerosol properties, and its impact is not limited to the
refractive indices. In this study, the smoke layers we observed were lofted
to the lower stratosphere in the source region and then transported to the
observation sites. They were isolated from other tropospheric aerosol sources
and not likely to mix with them during the transport. The relative humidity
in the stratospheric layer was below
10 %, according to the radiosonde measurements. Our study provides a
reference for aged smoke aerosols in a dry condition.

The retrieved particle parameters allow an estimation of direct aerosol
radiative forcing. We derived −79.6 W m-2τ-1 for the DRF
efficiency at the BOA for Lille data. And for Palaiseau data, we derived
−89.6 W m-2τ-1. This indicates that the observed stratospheric
aerosol layers strongly reduce the radiation reaching the terrestrial surface
mainly by absorbing solar radiation. Derimian et al. (2016)
evaluated the radiative effect of several aerosol models, among which the
daily net DRF efficiency of biomass burning aerosols is estimated to be −74
to −54 W m-2τ-1 at the BOA. Mallet et al. (2008) studied
the radiative forcing of smoke and dust mixtures over Djougou and derived
−68 to −50 W m-2τ-1 for the DRF efficiency at the BOA. Our
results are comparable with the values in the publications. Additionally, the
mean heating rate of the stratospheric smoke layer is estimated to be about
3.5 K day−1 for Lille and for Palaiseau data, which qualitatively
supports the temperature increase within the stratospheric smoke layer. The
warming effect in the layer is potentially responsible for the upward
movements of soot-containing aerosol plumes
(Laat et al., 2012; Ansmann et al., 2018). The high uncertainty in the
retrieved microphysical properties, especially the imaginary part of the
refractive indices, will propagate into the DRF estimation. At the current
stage, we are not able to accurately estimate the uncertainty in the
microphysical properties and in the DRF calculation. Varying the imaginary
part by ±50 %, we calculated the variability in the DRF efficiency at
the BOA and the heating rate, and we derived about 20 % variation in the
DRF efficiency at the BOA and 40 % variation in the heating rate.

In the summer of 2017, large-scale wildfires spread in the west and north of
Canada. The severe fire activities generated strong convections that lofted
smoke plumes up to the high altitudes. After long-range transport, the smoke
plumes spread over large areas. Three lidar systems in northern France
observed aged smoke plumes in the stratosphere, about 10–17 days after
the intense fire emissions in mid-August. Unlike fresh
smoke particles, the aged smoke particles showed surprisingly high particle
depolarization ratios, indicating the presence of irregular smoke particles.
Lidar data inversion revealed that the smoke particles are relatively bigger
compared to fresh smoke particles and very absorbent. The strong
absorption of the observed smoke plumes is related to the perturbation of the
temperature profile and the ascent of the plume when exposed to sunlight. In
addition, the DRF estimation indicated that the stratospheric smoke can
strongly reduce the radiation reaching the bottom of the atmosphere.

This study shows the capability of multi-wavelength Raman lidar in aerosol
profiling and characterization. We reported important optical and
microphysical properties derived from lidar observations; these results help
to improve our knowledge about smoke particles and aerosol classification,
which is an important topic in the lidar community. Future improvements in
better quantifying the uncertainty in the optical and microphysical
properties are highly anticipated. Moreover, this event is also a good
opportunity for the study of the atmospheric model. The injection of smoke
into the
upper troposphere and lower stratosphere by strong convection needs to be
considered in atmospheric models. The self-lifting of absorbing smoke is not
yet considered in any aerosol transport model. Additionally, this event
provides a favorable chance for studying smoke aging processes, the smoke
plumes stayed in the stratosphere more than 1 month and were observed by
ground-based lidars and CALIPSO. Much more effort is needed in
investigating these measurements.

The satellite data from OMPS and AIRS can be found in
NASA's GES DIS service center. CALIPSO data are obtained from the Langley
Atmospheric Science Data Center. The radiosonde data are taken from the
website of the University of Wyoming
(http://weather.uwyo.edu/upperair/sounding.html, last access:
15 January 2019). All the lidar data used in this paper and data
processing code or softwares are available upon request to the corresponding
author.

A1 Errors of optical depth

The errors in the lidar signal at the top and the base of the stratospheric
layers are considered to be the major error sources in the error estimation of
the optical depth. We estimate the error of the lidar signal
P‾(λ,rtop) and
P‾(λ,rbase) to be 3 %–5 %, based on the
statistical error of photon distributions. According to
Eq. (2), the error of the optical depth,
Δτuτu, is written as

where Δτu represents the absolute error of τu. The
calculation of molecular extinction and backscattering coefficient is based
on the study of Bucholtz (1995). The temperature and pressure
profiles are taken from the closest radiosonde stations, Trappes and
Beauvechain, and the errors of molecular scattering are neglected.

The error of optical depth propagates into the lidar ratio and vertically
integrated backscatter coefficient. Additionally, the error of the lidar
ratio also relies on the step width of lidar ratio between two consecutive
iterations and the fitting error of the optical depth of the stratospheric
aerosol layer, which can be limited by narrowing the step of the iteration.
In our calculation, we use a step of 0.5 sr and achieve the fitting error of
optical depth of less than 1 % which is negligible compared to the
contribution of the error of optical depth to the error of lidar ratio.
However, we can basically estimate the error of the integral of the
backscatter coefficient within the stratospheric aerosol layer, not the error
of the backscatter coefficient profile.

A2 Errors of Ångström exponent

Ångström exponent Å is defined as follows:

(A3)xλ1xλ2=λ1λ2-Å,

where x is usually the optical quantities such as optical depth τ,
extinction coefficient α, and backscatter coefficient β. The
error of the Ångström exponent results from the error of the
optical quantities at two involved wavelengths:

(A4)ΔÅ2=logλ1λ2-2Δxλ1xλ12+Δxλ2xλ22,

where Δx is the error of the quantity x in absolute values. When the error of the optical depth at 355 and
532 nm is approximately 15 %, the resulting error in the Ångström exponent is about 0.5.

A3 Errors of particle depolarization ratio

According to Eq. (3), the error of the particle depolarization
ratio lies in three terms: the backscatter ratio R, volume depolarization
ratio δv, and molecular depolarization ratio
δm.

(A5)Δδpδp2=FRΔRR2+FδvΔδvδv2+FδmΔδmδm2,(A6)FX=Xδp∂δp∂X2,X=R,δv,δm.

As the backscatter ratio and the volume depolarization increase, the
dependence of particle depolarization ratio on the backscatter ratio
decreases. In the stratospheric smoke layer, the measured volume
depolarization ratio is higher in the shorter wavelength and the backscatter
ratio is higher in the longer wavelength; the increased volume depolarization
ratio or the backscatter ratio allows us to conservatively assume a
preliminary error level for the backscatter ratio R. The potential error
sources of the volume depolarization come from the optics and the
polarization calibration. The optics have been carefully maintained and
adjusted to minimize the errors originating from misalignments. After
long-term lidar operation and monitoring of the depolarization calibration,
we conservatively expect 10 % relative errors in the volume
depolarization ratio. The theoretical molecular depolarization ratio is
calculated to be 0.0036 with negligible wavelength dependence
(Miles et al., 2001). In the historical record since 2013, LILAS measured
molecular depolarization ratios of approximately 0.005–0.013 at 532 nm,
0.012–0.018 at 355 nm, and 0.007–0.010 at 1064 nm. IPRAL measured a
molecular depolarization ratio of about 0.020 at 355 nm in this study.
Molecular depolarization ratios measured by both the LILAS and IPRAL systems
exceed the theoretical value. In addition to the error in the polarization
calibration, the error of molecular depolarization ratio arises mainly from
the optics, more precisely, the cross-talks between the two polarization
channels. The imperfections of the optics cannot be avoided, but a careful
characterization is helpful to eliminate the cross-talks as much as possible
(Freudenthaler, 2016). In our study, we simply assume 200 %
and 300 % for the error of molecular depolarization ratio measured by the
LILAS and IPRAL systems, respectively. The total error of the particle
depolarization ratio is calculated according to Eq. (A5).

QH carried out the experiments at the Lille station,
processed the data, and wrote the paper. PG and OD supervised the project
and helped with paper correction. IV helped in the data analysis and
paper correction. JBA contributed in providing IPRAL measurements and
paper correction. IEP performed experiments using the MAMS system, analyzed
the data, and helped with paper corrections. TP contributed in LILAS measurements
and calibration (with QH). MH and CP helped in paper correction and
IPRAL operation. AL and XH contributed in developing and implementing
the GARRLiC/GRASP algorithm and radiative transfer code, respectively. CC helped
in obtaining and interpreting satellite products. BT helped in paper
correction.

We wish to thank the ESA/IDEAS program (ESRIN/VEGA 4000111304/14/I-AM), who
supported this work. FEDER/Region Hauts-de-France and CaPPA Labex are
acknowledged for their support for the LILAS multi-wavelength Raman lidar and
MAMS systems. H2020-ACTRIS-2/LiCAL calibration center, EARLINET,
ACTRIS-France, ANRT France, CIMEL Electronique, and Service National
Observation PHOTONS/AERONET from CNRS-INSU are acknowledged for their
support. The development of lidar retrieval algorithms was supported by the
Russian Science Foundation (project 16-17-10241). The authors would like to
acknowledge the use of the GRASP inversion algorithm
(http://www.grasp-open.com, 16 January 2019) in this work. NASA and the
Langley Atmospheric Science Data Center are acknowledged for providing
satellite products. The SIRTA observatory and supporting institutes are
acknowledged for providing IPRAL data. Finally we thank all the co-authors
for their kind cooperation and professional
help.

Smoke plumes generated in Canadian fire activities were elevated to the lower stratosphere and transported from North America to Europe. The smoke plumes were observed by three lidar systems in northern France. This study provides a comprehensive characterization for aged smoke aerosols at high altitude using lidar observations. It presents that fire activities on the Earth's surface can be an important contributor of stratospheric aerosols and impact the Earth's radiation budget.

Smoke plumes generated in Canadian fire activities were elevated to the lower stratosphere and...